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Background: This study seeks to build a two-stage deep learning approach for identifying the microsatellite instability (MSI) status of colon cancer based on computed tomography (CT) scans without the requirement for manual segmentation. Methods: This study included 108 enhanced CT scans of colon cancer, including 68 cases of ascending colon, 14 cases of transverse colon, 18 cases of descending colon, and 8 cases of sigmoid colon; there were 56 cases of MSI-H and 52 cases of microsatellite stability (MSS). In the first stage, the segmentation model MSI-SAM was trained to accurately segment the lesion locations in the CT scans. In the second stage, the mask acquired from the MSI-SAM segmentation was multiplied by the original CT image (CTOrigin) bitwise, and the result was merged with the mask obtained from the MSI-SAM segmentation (Segment) to obtain CTROI. Both CTROI and CTOrigin were then diagnosed using the colon cancer MSI status diagnosis model. Results: The performance of the suggested CT segmentation model MSI-SAM in the ascending colon, transverse colon, descending colon, and sigmoid colon areas (DSC: IoU) was (0. 886: 0. 798), (0. 878: 0. 783), (0. 923: 0. 857), and (0. 854: 0. 747), respectively. The AUC of the MSI status diagnostic model for patients with colon cancer was 0. 935 (95% CI 0. 892-0. 947), the ACC was 0. 913, the sensitivity was 1. 000, and the specificity was 0. 846. Conclusions: The segmentation masks created by the trained deep learning segmentation model achieved a level comparable to that of expert radiologists, and the deep learning diagnostic model played an essential role in supporting doctors in diagnosis.
Cui et al. (Mon,) studied this question.